44 modelling-complexity-geocomputation PhD positions at Technical University of Munich in Germany
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features with an emphasis on vegetation complexity, urban infrastructure, and bird diversity. The thesis is part of a greater project studying urban green space and soundscapes and how these relate to human
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impact and adaptation analyses, and for spatio-temporal modelling as well as upscaling of ecosystem properties via remote sensing Interactive collaboration and exchange within the TUM Center for Forest
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
interfaces. Topics of interest include: Planar and geometric graph algorithms Approximation and parameterized algorithms Clustering, embeddings, and structural graph theory Computational complexity and
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an exceptional international team with expertise in all aspects of the project. Your tasks will include: • Preparation of different EO and in-situ datasets for training a machine learning model • Development of ML
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messengers transported by the flow or even the pressure of the fluid itself. In an interdisciplinary team, you will either develop theoretical models of the feedback between flow and network architecture
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efficient and safe trajectory planning in safety-critical scenarios. For this purpose, we focus on modeling and quantifying risks in order to subsequently incorporate them into trajectory planning. The goal
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resource efficiency. A physics-based model for monitoring the condition of helicopter components is being developed as part of this project. With the help of flight test data, this model is to be calibrated
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organized manner and pay attention to details. ▪ You thrive on finding solutions to complex problems. ▪ You have strong communication and writing skills. What can you expect in return: ▪ A curiosity-driven
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networks involved in CHC perception, particularly in the context of prezygotic reproductive isolation within a species complex of parasitoid wasps (Nasonia). Our previous research has already deciphered
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at start: • Strong background in T cell biology/immunology. • Hands-on experience with transgenic mouse models, including breeding and colony management. • Proficiency in preparing and processing lymphoid